Recommendation systems (RS) have become indispensable tools for web services to address information overload, thus enhancing user experiences and bolstering platforms' revenues. However, with their increasing ubiquity, security concerns have also emerged. As the public accessibility of RS, they are susceptible to specific malicious attacks where adversaries can manipulate user profiles, leading to biased recommendations. Recent research often integrates additional modules using generative models to craft these deceptive user profiles, ensuring them are imperceptible while causing the intended harm. Albeit their efficacy, these models face challenges of unstable training and the exploration-exploitation dilemma, which can lead to suboptimal results. In this paper, we pioneer to investigate the potential of diffusion models (DMs), for shilling attacks. Specifically, we propose a novel Target-oriented Diffusion Attack model (ToDA). It incorporates a pre-trained autoencoder that transforms user profiles into a high dimensional space, paired with a Latent Diffusion Attacker (LDA)-the core component of ToDA. LDA introduces noise into the profiles within this latent space, adeptly steering the approximation towards targeted items through cross-attention mechanisms. The global horizon, implemented by a bipartite graph, is involved in LDA and derived from the encoded user profile feature. This makes LDA possible to extend the generation outwards the on-processing user feature itself, and bridges the gap between diffused user features and target item features. Extensive experiments compared to several SOTA baselines demonstrate ToDA's effectiveness. Specific studies exploit the elaborative design of ToDA and underscore the potency of advanced generative models in such contexts.

翻译：推荐系统（RS）已成为网络服务应对信息过载不可或缺的工具，既能提升用户体验，也能增加平台收益。然而，随着其日益普及，安全问题也逐渐显现。由于RS的公开可访问性，它们易受特定恶意攻击，攻击者可操纵用户画像，导致推荐结果产生偏差。近期研究常通过生成模型集成额外模块来构建这些欺骗性用户画像，确保其隐蔽性的同时造成预期危害。尽管这些方法有效，但仍面临训练不稳定与探索-利用困境的挑战，可能导致次优结果。本文率先探索扩散模型（DMs）在托攻击中的潜力。具体而言，我们提出了一种新型面向目标的扩散攻击模型（ToDA）。该模型融合预训练自编码器，将用户画像映射至高维空间，并协同使用ToDA的核心组件——潜在扩散攻击器（LDA）。LDA在此潜在空间中对画像引入噪声，通过交叉注意力机制巧妙引导近似目标物品。基于二部图实现的全局视角被纳入LDA，该视角源自编码后的用户画像特征。这使得LDA能够将生成范围扩展至当前处理的用户特征之外，并弥合扩散后用户特征与目标物品特征之间的鸿沟。与多个最先进基线的广泛实验表明ToDA的有效性。专项研究深入剖析了ToDA的精细化设计，凸显了高级生成模型在此类场景中的潜力。